Search Results (159)

As AI decision support systems play a growing role in high-stakes decision making, ensuring effective integration of human intuition with AI recommendations is essential. Despite advances in AI explainability, challenges persist in fostering appropriate reliance. This review explores AI decision support systems that enhance human intuition through the analysis of 84 studies addressing three questions: (1) What design strategies enable AI systems to support humans’ intuitive capabilities while maintaining decision-making autonomy? (2) How do AI presentation and interaction approaches influence trust calibration and reliance behaviors in human–AI collaboration? (3) What ethical and practical implications arise from integrating AI decision support systems into high-risk human decision making, particularly regarding trust calibration, skill degradation, and accountability across different domains? Our findings reveal four key design strategies: complementary role architectures that amplify rather than replace human judgment, adaptive user-centered designs tailoring AI support to individual decision-making styles, context-aware task allocation dynamically assigning responsibilities based on situational factors, and autonomous reliance calibration mechanisms empowering users’ control over AI dependence. We identified that visual presentations, interactive features, and uncertainty communication significantly influence trust calibration, with simple visual highlights proving more effective than complex presentation and interactive methods in preventing over-reliance. However, a concerning performance paradox emerges where human–AI combinations often underperform the best individual agent while surpassing human-only performance. The research demonstrates that successful AI integration in high-risk contexts requires domain-specific calibration, integrated sociotechnical design addressing trust calibration and skill preservation simultaneously, and proactive measures to maintain human agency and competencies essential for safety, accountability, and ethical responsibility. Full article

Federated learning (FL) is emerging as a pivotal paradigm for environmental monitoring, enabling decentralized model training across edge devices without exposing raw data. This review provides the first structured synthesis of 361 peer-reviewed studies, offering a comprehensive overview of how FL has been implemented across environmental domains such as air and water quality, climate modeling, smart agriculture, and biodiversity assessment. We further provide comparative insights into model architectures, energy-aware strategies, and edge-device trade-offs, elucidating how system design choices influence model stability, scalability, and sustainability. The analysis traces the technological evolution of FL from communication-efficient prototypes to robust, context-aware deployments that integrate domain knowledge, physical modeling, and ethical considerations. Persistent challenges remain, including data heterogeneity, limited benchmarking, and inequitable access to computational infrastructure. Addressing these requires advances in hybrid physics–AI frameworks, privacy-preserving sensing, and participatory governance. Overall, this review positions FL not merely as a technical mechanism but as a socio-technical shift—one that aligns distributed intelligence with the complexity, uncertainty, and urgency of contemporary environmental science. Full article

Achieving the decarbonization of the building stock by 2050 requires not only technological innovation but also strategies capable of addressing energy poverty, which threatens to exclude millions of households from a fair transition. Measuring this phenomenon remains challenging: at the European level, monitoring systems rely mainly on aggregated statistics, useful for territorial comparisons but often too approximate to describe the conditions of individual households and dwellings. This paper proposes a building-scale methodology that integrates socio-economic and technical data collected directly through surveys, interviews, and utility bills. The approach was applied to a private multi-family building built in the early twentieth century in Turin (Italy), involving 16 households. Results indicate that 31% of households exceed the 10% energy expenditure threshold, with heating emerging as the main cost driver. Correlation analyses suggest that single parameters such as income or dwelling size are not sufficient on their own to explain vulnerability, whereas the integration of socio-technical factors provides a more detailed picture of household conditions. Based on this evidence, four intervention strategies were developed, ranging from the insulation of the envelope to the installation of photovoltaics, conceived to be implemented progressively according to real technical and economic constraints. The novelty of this study lies in linking building-scale evidence with concrete design solutions, bridging the gap between measurement and action. Full article

This study investigates the accelerating adoption of electric vehicles (EVs) integrated with residential rooftop solar and battery storage in Australia, employing Actor–Network Theory (ANT) to elucidate socio-technical dynamics. Through purposive sampling, semi-structured interviews with 15 EV industry stakeholders were conducted and analysed using NVivo 14. Findings revealed EV–solar–storage adoption as a negotiated process shaped by alignments among human and non-human actors, structured by three interdependent obligatory passage points. First, technological integration hinges on interoperability among inverters, smart chargers, EV supply equipment, batteries, and home energy management systems. These are constrained by factors like off-street parking availability. Second, policy and market frameworks require clear interconnection standards, bidirectional charging protocols, streamlined approvals, and targeted incentives. Third, consumer engagement depends on energy literacy, equitable access for renters, and daytime charging infrastructure. Smart and bidirectional charging positions EVs as flexible energy assets, yet gaps in standards and awareness destabilise networks. This ANT-framed study offers a practice-oriented model for clean mobility integration, proposing targeted interventions such as device compatibility standards, equitable policies, and education to maximise environmental and economic benefits at household and system levels. Full article

Industrial engineering has long served as a cornerstone of productivity and efficiency in manufacturing environments by focusing on the design and optimization of machinery, processes, and systems. However, its application has largely remained confined within the traditional boundaries of factory floors. This narrow scope has limited its potential in addressing broader, systemic challenges in a rapidly evolving industrial landscape. This research identifies a significant gap: despite its foundational role in operations, industrial engineering has not fully adapted to the demands of Industry 4.0 and the emerging paradigms of Industry 5.0, which emphasize human–machine harmony, sustainability, and adaptability. This paper advocates for a revolution in industrial engineering—one that transcends conventional methods and redefines the discipline through open-minded innovation, universal applicability, and immediate transformation. The novelty of this review lies in its conceptual framework that promotes optimization as a mindset rather than a rigid methodology. It argues that industrial engineering must evolve into a dynamic discipline capable of creative problem-solving, unrestricted by outdated procedures or limited applications. This paper outlines three key transformations required to achieve this revolution: (1) the universal application of industrial engineering principles beyond traditional domains; (2) the prioritization of innovation and creativity over procedural optimization; and (3) the urgency of immediate implementation. By challenging conventional thinking and encouraging the development of novel, potentially patentable approaches, this study aims to position industrial engineering at the forefront of technological revolutions and socio-technical change. This revolutionary perspective is intended to guide both academics and practitioners in embracing a more fluid, adaptive, and forward-looking role, ensuring that industrial engineering remains relevant and impactful in shaping the future of global industry in the context of Industry 4.0 and beyond. Full article

The construction industry is one of the largest consumers of resources and a significant contributor to environmental degradation in Europe, accounting for 50% of natural resource use, 34% of waste generation, and 5–12% of greenhouse gas emissions. In response to growing environmental pressures and regulatory demands, the sector needs to adopt sustainable material alternatives. This study examines the potential adoption of rice straw ash in the European construction sector. The research applies a PRISMA-based systematic literature review, integrated with the Multi-Level Perspective (MLP) framework, PESTLE, and SWOT analyses to provide a comprehensive assessment of the socio-technical dynamics influencing its adoption. The findings identify barriers including the absence of standards, fragmented supply chains, and inconsistent material quality. However, it highlights strategic opportunities such as the declining availability of conventional SCMs, alignment with the EU’s regulations and circular economy principles, and growing public awareness of sustainable materials. The study concludes that advancing the transition to RSA will require regulatory support, the development of standards, and coordinated collaboration among stakeholders to achieve large-scale implementation. By integrating multi-dimensional transition factors, this research contributes actionable insights for advancing sustainable material adoption. Full article

Green hydrogen offers a promising yet underexplored pathway for agricultural decarbonisation, requiring technological readiness and coordinated action from policymakers, industry, and farmers. This paper integrates techno-economic modelling with stakeholder engagement (semi-structured interviews and an expert workshop) to assess its potential. Analyses were conducted for farms of 123 hectares and clusters of 10 farms, complemented by seven interviews and a workshop with nine sector experts. Findings show both opportunities and barriers. While on-farm hydrogen production is technically feasible, it remains economically uncompetitive due to high levelised costs, shaped by seasonal demand variability and low utilisation of electrolysers and storage. Pooling demand across multiple users is essential to improve cost-effectiveness. Stakeholders identified three potential business models: fertiliser production via ammonia synthesis, cooperative-based models, and local refuelling stations. Of these, cooperative hydrogen hubs emerged as the most promising, enabling clusters of farms to jointly invest in renewable-powered electrolysers, storage, and refuelling facilities, thereby reducing costs, extending participation to smaller farms, and mitigating risks through collective investment. By linking techno-economic feasibility with stakeholder perspectives and business model considerations, the results contribute to socio-technical transition theory by showing how technological, institutional, and social factors interact in shaping hydrogen adoption in agriculture. With appropriate policy support, cooperative hubs could lower costs, ease concerns over affordability and complexity, and position hydrogen as a practical driver of agricultural decarbonisation and rural resilience. Full article

This study examines the challenges of implementing educational technologies for sustainability education in diverse, real-world settings. While such tools are often designed for universal applications, a multitude of contextual factors, particularly in low-resource scenarios, can impede their full implementation. Through a series of in situ experiments conducted across three educational settings in Greece, Romania, and Italy, the research revealed that field deployment yields critical insights into organisational and technical limitations that are not evident in controlled experiments. The key findings underscore the importance of incorporating a broad range of socio-technical factors into design research protocols. The research also reveals a significant trade-off between the readiness of a tool and the need for its contextualisation, underscoring that effective implementation requires iterative adaptation and tailored training. Ultimately, the work concludes that real-world deployment blurs the distinction between a prototype and a product, necessitating a flexible approach to ensure equitable and prosperous adoption. Full article

Structural collapses are a major threat to urban safety and infrastructure resilience and as such there is growing research interest in understanding the causes and improving the prediction of risk to prevent human and material losses. Whether caused by fires, earthquakes or progressive failures due to overloads and displacements, these events have been the focus of investigation over the past 15 years. This systematic literature review looks at the use of formal risk analysis models in structural failures between 2010 and 2025 to map methodological trends, assess model effectiveness and identify future research pathways. From an initial database of 139 documented collapse incidents, only 42 were investigated using structured risk analysis frameworks. A systematic screening of 417 related publications yielded 101 peer-reviewed studies that met our inclusion criteria—specifically, the application of a formal analytical model. This discrepancy highlights a significant gap between the occurrence of structural failures and the use of rigorous, model-based investigation methods. The review shows a clear shift from single-method approaches (e.g., Fault Tree Analysis (FTA) or Finite Element Analysis (FEA)) to hybrid, integrated models that combine computational, qualitative and data-driven techniques. This reflects the growing recognition of structural failures as socio-technical phenomena that require multi-methodological analysis. A key contribution is the development of a strategic framework that classifies models by complexity, data requirements and cost based on patterns observed across the reviewed papers. This framework can be used as a practical decision support tool for researchers and practitioners to select the right model for the context and highlight the strengths and limitations of the existing approaches. The findings show that the future of structural safety is not about one single “best” model but about intelligent integration of complementary context-specific methods. This review will inform future practice by showing how different models can be combined to improve the depth, accuracy and applicability of structural failure investigations. Full article

Background: Accurate prediction of postoperative care requirements is critical for patient safety and resource allocation. Although numerous approaches involving artificial intelligence (AI) and machine learning (ML) have been proposed to support such predictions, their implementation in practice has so far been insufficiently successful. One reason for this is that the performance of the algorithms is difficult to assess in practical use, as the accuracy of clinical decisions has not yet been systematically quantified. As a result, models are often assessed purely from a technical perspective, neglecting the socio-technical context. Methods: We conducted a retrospective, single-center observational study at the University Hospital Augsburg, including 35,488 elective surgical cases documented between August 2023 and January 2025. For each case, preoperative care-level predictions by surgical and anesthesiology teams were compared with the actual postoperative care provided. Predictive performance was evaluated using accuracy and sensitivity. Since this is a highly imbalanced dataset, in addition to sensitivity and specificity, the balanced accuracy and the $F_{\beta }$-score were also calculated. The results were contrasted with published Machine-Learning (ML)-based approaches. Results: Overall prediction accuracy was high (surgery: 91.2%; anesthesiology: 87.1%). However, sensitivity for identifying patients requiring postoperative intensive care was markedly lower than reported for ML models in the literature, with the largest discrepancies observed in patients ultimately admitted to the ICU (surgery: 38.05%; anesthesiology: 56.84%; ML: 70%). Nevertheless, clinical judgment demonstrated a superior $F_1$-score, indicating a more balanced performance between sensitivity and precision (surgery: 0.527; anesthesiology: 0.551; ML: 0.28). Conclusions: This study provides the first real-world benchmark of clinical expertise in postoperative care prediction and shows a way in which modern ML approaches must be evaluated in a specific sociotechnical context. By quantifying the predictive performance of surgeons and anesthesiologists, it enables an evaluation of existing ML approaches. Thus the strength of our work is the provision of a real-world benchmark against which all ML methods for preoperative prediction of ICU demand can be systematically evaluated. This enables, for the first time, a comparison of different approaches on a common, practice-oriented basis and thus significantly facilitates translation into clinical practice, thereby closing the translational gap. Furthermore it offers a data-driven framework to support the integration of ML into preoperative decision-making. Full article

This paper introduces cognitive adaptivity as a novel framework for addressing human factors in cybersecurity during the Industry 5.0–6.0 transition, with a focus on hard-to-abate industries where digital transformation intersects sustainability constraints. While the integration of IoT, automation, digital twins, and artificial intelligence expands industrial efficiency, it simultaneously exposes organizations to increasingly sophisticated social engineering and AI-powered attack vectors. Traditional resilience-based models, centered on recovery to baseline, prove insufficient in these dynamic socio-technical ecosystems. We propose cognitive adaptivity as an advancement beyond resilience and antifragility, defined by three interrelated dimensions: learning, anticipation, and human–AI co-evolution. Through an in-depth case study of the ceramic value chain, this research develops a conceptual model demonstrating how organizations can embed trust calibration, behavioral evolution, sustainability integration, and systemic antifragility into their cybersecurity strategies. The findings highlight that effective protection in Industry 6.0 environments requires continuous behavioral adaptation and collaborative intelligence rather than static controls. This study contributes to cybersecurity literature by positioning cognitive adaptivity as a socio-technical capability that redefines the human–AI interface in industrial security. Practically, it shows how organizations in hard-to-abate sectors can align cybersecurity governance with sustainability imperatives and regulatory frameworks such as the CSRD, turning security from a compliance burden into a strategic enabler of resilience, competitiveness, and responsible digital transformation. Full article

This study examines digital hospitality as a socio-technical system in which technological adoption and human resource (HR) practices jointly shape guest experiences and workforce dynamics. The research is situated at CitizenM hotels in Paris, a brand recognized for its integration of mobile applications, automated check-in, and the ambassador model of flexible role design. A mixed-methods approach was applied, combining a guest survey (n = 517) with semi-structured interviews with managers. Exploratory and confirmatory factor analyses confirmed a five-factor structure of guest perceptions: Digital Efficiency, Smart Personalization, Service Satisfaction, Trusted Security, and Digital Loyalty. Structural equation modeling showed that efficiency significantly drives satisfaction, while personalization and security strongly predict loyalty. Managerial insights revealed that these outcomes rely on continuous investment in training, mentorship, and flexible role allocation. Overall, the findings suggest that digital transformation enhances value creation not by substituting but by reconfiguring human service, with technology alleviating routine tasks and enabling employees to focus on relational and creative aspects of hospitality. The study concludes that effective digital hospitality requires the alignment of technological innovation with supportive HR practices, ensuring both guest satisfaction and employee motivation. Full article

Despite the rapid adoption of artificial intelligence (AI) on a global scale, a comprehensive framework that maps its end-to-end value chain is missing. The presented study employed a multi-layered framework to analyze the value creation and delivery mechanism of the five core layers of an AI value chain, including (1) hardware, (2) data management, (3) foundational AI, (4) advanced AI capabilities, and (5) AI delivery. Using a qualitative–descriptive approach with a multi-faceted thematic analysis and a SWOT-based bottleneck analysis of each core layer, the study maps a sequential value flow from a globally dependent hardware foundation to the deployment of AI services. The analysis reveals that international knowledge flows shape the ecosystem, while the “last-mile” integration challenge is not merely a technical issue; instead, it highlights a significant socio-technical disconnect between technological advancements and the preparedness of the workforce. This study provides a holistic framework that frames the AI value chain as a socio-technical system, offering critical insights for stakeholders. The findings emphasize that unlocking AI’s full potential requires strategic investment in the managerial competencies and digital skills that constitute human–capital readiness. Full article

Engineers are often faced with complex problems that require both technical and social expertise. However, typical engineering curricula teach technical skills in isolation, without introducing social issues. To address this gap, we implemented a sociotechnical module that linked the circuits topic of capacitors with the social issue of conflict minerals in a single class session of a large “Introduction to Circuits” course. Using a midterm student feedback survey and student group interviews, we explored students’ responses to the module, their takeaways, and their general attitudes towards sociotechnical content in technical engineering courses. Overall, students found the module to be valuable and relevant, with many noting that it helped them understand real-world engineering practice. While some expressed concern about adding new material to an already content-heavy course, more than half agreed that this type of content is important and that they would like to see more sociotechnical topics in their engineering courses. Full article

Decentralized electricity markets are increasingly shaped by the proliferation of distributed energy resources, the rise of prosumers, and growing demands for privacy-aware analytics. In this context, federated learning (FL) emerges as a promising paradigm that enables collaborative model training without centralized data aggregation. This review systematically explores the application of FL in energy systems, with particular attention to architectures, heterogeneity management, optimization tasks, and real-world use cases such as load forecasting, market bidding, congestion control, and predictive maintenance. The article critically examines evaluation practices, reproducibility issues, regulatory ambiguities, ethical implications, and interoperability barriers. It highlights the limitations of current benchmarking approaches and calls for domain-specific FL simulation environments. By mapping the intersection of technical design, market dynamics, and institutional constraints, the article formulates a pluralistic research agenda for scalable, fair, and secure FL deployments in modern electricity systems. This work positions FL not merely as a technical innovation but as a socio-technical intervention, requiring co-design across engineering, policy, and human factors. Full article